--- title: "Box vs OGAM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jegly-box-vs-off-grid-ai-off-grid-ai-mobile" tools: ["jegly-box", "off-grid-ai-off-grid-ai-mobile"] --- # Box vs OGAM *GraphCanon updated Sep 20, 2026* ## Verdict Pick Box if box, an offline AI suite for Android devices with capabilities in music generation and visionAI; pick OGAM if oGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need. [Box](https://jegly.xyz) reports 843 GitHub stars, 53 forks, and 4 open issues, last pushed Sep 3, 2026. [OGAM](https://getoffgridai.co/pro/) has 3.1k stars, 298 forks, and 151 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [Box's repository](https://github.com/jegly/Box) and [OGAM's repository](https://github.com/off-grid-ai/OGAM). | | [Box](/tools/jegly-box.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Tagline | fully offline client-side AI suite for Android | Swiss Army Knife of Offline AI | | Stars | 843 | 3,124 | | Forks | 53 | 298 | | Open issues | 4 | 151 | | Language | Kotlin | TypeScript | | Adopt for | Box, an offline AI suite for Android devices with capabilities in music generation and visionAI. | OGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need | | Persona | - | - | | Runtime | - | - | | License | Other | MIT License, allowing for free use, modification, and distribution of the software. | | Categories | Inference & Serving, Model Training | Computer Vision, Developer Tools, Inference & Serving, LLM Frameworks, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Box](/tools/jegly-box.md) | [OGAM](/tools/off-grid-ai-off-grid-ai-mobile.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 16d | 0d | | Open issues (now) | 4 | 151 | | Stars delta | +96 (30d) | +269 (30d) | | Open issues delta | +1 (30d) | +14 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/jegly-box/trust.md) | [trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust.md) | ## Decision facts: Box - **Adopt for:** Box, an offline AI suite for Android devices with capabilities in music generation and visionAI. ## Decision facts: OGAM - **Pricing:** freemium - The core functionality is free, but premium features or services may be available for purchase. - **Requirements:** Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices. - **Adopt for:** OGAM is a versatile offline AI toolkit that supports a wide range of functionalities including chat, vision, speech-to-text, and image generation, all of which can be performed on mobile and Mac devices without the need - **License detail:** MIT License, allowing for free use, modification, and distribution of the software. ## Choose when ### Choose Box if… - Box is primarily Kotlin; OGAM is TypeScript. - License: Box is Other, OGAM is MIT. - Tags unique to Box: music-generation, offline-operation, visionai. - Also covers Model Training. - When targeting privacy-sensitive tasks that require full offline functionality on Android devices ### Choose OGAM if… - OGAM is primarily TypeScript; Box is Kotlin. - License: OGAM is MIT, Box is Other. - Pricing: The core functionality is free, but premium features or services may be available for purchase.. - Requirements: Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices.. - Tags unique to OGAM: edge-ai, gguf, ios, llama-cpp. - Also covers Computer Vision, Developer Tools, LLM Frameworks, Speech & Audio. - OGAM ships an MCP server manifest. - When you need a comprehensive offline AI toolkit that supports chat, vision, speech-to-text, and image generation on mobile and Mac devices. ## When NOT to use Box - If your project requires access to a broad range of AI models not supported by Box - In scenarios where continuous model updates via internet are necessary for accuracy and performance ## When NOT to use OGAM - If you need real-time cloud-based AI services that require internet access for data processing and model updates. - When you are working in an environment where the use of local AI models is restricted or not supported by the hardware available. - If your project requires integration with specific cloud APIs or services that OGAM does not support due to its offline nature. ## Common questions ### What is the difference between Box and OGAM? Box: fully offline client-side AI suite for Android. OGAM: Swiss Army Knife of Offline AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose Box over OGAM? Choose Box over OGAM when Box is primarily Kotlin; OGAM is TypeScript; License: Box is Other, OGAM is MIT; Tags unique to Box: music-generation, offline-operation, visionai; Also covers Model Training; When targeting privacy-sensitive tasks that require full offline functionality on Android devices. ### When should I choose OGAM over Box? Choose OGAM over Box when OGAM is primarily TypeScript; Box is Kotlin; License: OGAM is MIT, Box is Other; Pricing: The core functionality is free, but premium features or services may be available for purchase.; Requirements: Min 4 GB RAM; Supports CPU, GPU, and NPU for running AI models.; Available for Android, iOS, and macOS devices.; Tags unique to OGAM: edge-ai, gguf, ios, llama-cpp; Also covers Computer Vision, Developer Tools, LLM Frameworks, Speech & Audio; OGAM ships an MCP server manifest; When you need a comprehensive offline AI toolkit that supports chat, vision, speech-to-text, and image generation on mobile and Mac devices. ### When should I avoid Box? If your project requires access to a broad range of AI models not supported by Box In scenarios where continuous model updates via internet are necessary for accuracy and performance ### When should I avoid OGAM? If you need real-time cloud-based AI services that require internet access for data processing and model updates. When you are working in an environment where the use of local AI models is restricted or not supported by the hardware available. If your project requires integration with specific cloud APIs or services that OGAM does not support due to its offline nature. ### Is Box or OGAM more popular on GitHub? OGAM has more GitHub stars (3,124 vs 843). Stars measure visibility, not whether either tool fits your constraints. ### Are Box and OGAM open source? Yes - both are open-source projects on GitHub (Box: Other, OGAM: MIT). ### Where can I find alternatives to Box or OGAM? GraphCanon lists graph-backed alternatives at [Box alternatives](/tools/jegly-box/alternatives) and [OGAM alternatives](/tools/off-grid-ai-off-grid-ai-mobile/alternatives) ([Box markdown twin](/tools/jegly-box/alternatives.md), [OGAM markdown twin](/tools/off-grid-ai-off-grid-ai-mobile/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/jegly-box-vs-off-grid-ai-off-grid-ai-mobile.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Box or OGAM? Box: Active. OGAM: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Box and OGAM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Box trust report](/tools/jegly-box/trust); [OGAM trust report](/tools/off-grid-ai-off-grid-ai-mobile/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jegly-box`](/api/graphcanon/graph?tool=jegly-box) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_